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Lab 2: Neural Network Implementation

This project implements a neural network using PyTorch and TorchVision. It includes training and evaluation of a ResNet-18 model on the CIFAR-10 dataset. The project allows customization of hyperparameters such as learning rate, optimizer, and computing device.

Requirements

  • Python 3.x
  • PyTorch
  • TorchVision

Install the required libraries using:

pip install torch torchvision

Usage

Run with Default Parameters

python lab2.py

Run with Custom Parameters

python lab2.py --lr 0.01 --device cuda --opt adam --c7

Parameters Desctiption

Parameter Description Default Value Options/Constraints
--lr Learning rate 0.1 Must be a float < 1
--device Computing device cpu cpu, cuda
--num_workers Number of I/O workers 2 Must be an integer
--data_path Dataset download directory ./data Path to directory
--opt Optimizer selection sgd sgd, nesterov, adam, adagrad, adadelta
--c7 Disable batch normalization in ResNet-18 False Set to True to disable batch normalization

Project Structure

├── lab2.py               # Main script for training and evaluation
├── plotting.py           # Script for generating figures
├── README.md             # Project documentation
└── data/                 # Directory for storing the dataset (default)
Additional Files
plotting.py: Generates figures for the report, such as training loss and test accuracy plots.

Example Commands

Train the model with a learning rate of 0.01 on a GPU using the Adam optimizer:

python lab2.py --lr 0.01 --device cuda --opt adam

Train the model without batch normalization:

python lab2.py --c7 Output The script will print the training loss and test accuracy for each epoch. Example output:

Epoch 1, Train Loss: 1.2345, Test Accuracy: 0.5678
Epoch 2, Train Loss: 1.1234, Test Accuracy: 0.6789

Notes

Ensure the dataset is downloaded to the correct directory (./data by default).

Experiment with different hyperparameters to observe their impact on performance.

If you encounter GPU memory issues, reduce the batch size or switch to the CPU.